Aparna Dhinakaran
Aparna Dhinakaran is the Co-Founder and Chief Product Officer at Arize AI, a pioneer and early leader in AI & Agent observability and evaluation. A frequent speaker at top conferences and thought leader in the space, Dhinakaran was recently named to the Forbes 30 Under 30. Before Arize, Dhinakaran was an ML engineer and leader at Uber, Apple, and TubeMogul (acquired by Adobe). During her time at Uber, she built several core ML Infrastructure platforms, including Michealangelo. She has a bachelor’s from Berkeley's Electrical Engineering and Computer Science program, where she published research with Berkeley's AI Research group. She is on a leave of absence from the Computer Vision Ph.D. program at Cornell University.
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AI EngineeringYou chose the best model. Why is your agent still failing?
Public benchmarks can show how a model performs in general. Production reliability depends on the context and harness around it, which only your team can evaluate against its… Aparna Dhinakaran Prukalpa Sankar August 12, 2026 12 min read -
Agent ObservabilityFrom Signal to PR: What if your agents got better every time they failed?
Signal, a managed agent built into Arize AX, continuously reviews production traces, surfaces ranked issues with evidence and proposed fixes, and — with Managed Agents — can carry… Chris Cooning Sally-Ann DeLucia Jason Lopatecki Aparna Dhinakaran July 29, 2026 5 min read -
Agent EngineeringWhat is a loop in AI engineering, anyway?
The AI engineering world is using “loop” to describe several different agent architectures. This post maps execution loops, task loops, product loops, system loops, and the human oversight… Aparna Dhinakaran Laurie Voss July 10, 2026 10 min read -
Agent EngineeringOwn the loop: A field guide to agent harnesses
As models become cheaper and more interchangeable, the durable advantage shifts to the agent harness: the loop, tools, memory, permissions, and workflow you can own and refine. Aparna Dhinakaran July 6, 2026 8 min read -
Agent EngineeringPostgresFS vs. SQL skills: should AI agents fake a filesystem?
Can an AI agent use a database as if it were a filesystem? Arize compared a Postgres-backed filesystem abstraction with a SQL skill and found that locality, accuracy,… Aparna Dhinakaran Sufjan Fana June 11, 2026 12 min read -
Agent EngineeringBuilding the AI factory for self-improving agents: What’s new in Arize AX
Arize AX is adding managed agents, full-agent experimentation, expanded multimodal support, and Harness-as-a-Judge to help teams observe, evaluate, and improve production agents. Jason Lopatecki Aparna Dhinakaran June 4, 2026 8 min read -
Agent EngineeringHow Hermes implements an open source agent harness architecture
Hermes from NousResearch is a strong open-source agent harness. This post examines how its runtime loop, context management, tool scoping, session infrastructure, and orchestration patterns map to a… Aparna Dhinakaran June 1, 2026 7 min read -
Agent EngineeringSwarm management in agent harnesses: owning long-running agents
As we have built our own harness management tools internally at Arize, and watched external systems like Devin @cognition start managing other Devins, managed agents at @AnthropicAI and… Aparna Dhinakaran May 4, 2026 11 min read -
Agent EngineeringContext management in agent harnesses: memory, files, and subagents
A version of this article originally appeared on X. Every agent harness runs into the same limit: the context window is too small for everything the model might want… Aparna Dhinakaran April 28, 2026 15 min read -
Agent EngineeringWhat is an agent harness?
A version of this article originally appeared on X. Someone asked me at a hacker event last week: “Can anyone actually tell me what a harness really is?”… Aparna Dhinakaran April 24, 2026 10 min read
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Arize gives AI teams observability and evals to understand and improve agent performance.
